HAL Id: hal-02170450
https://hal.archives-ouvertes.fr/hal-02170450
Submitted on 2 Jun 2020HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.
Distributed under a Creative Commons Attribution| 4.0 International License
Linking cocoa polyphenol composition to chocolate
quality with Average-Mass-Spectra fingerprints
Noémie Fayeulle, Emmanuelle Meudec, Arnaud Verbaere, Jean Claude Boulet,
Clotilde Hue, Renaud Boulanger, Veronique Cheynier, Nicolas Sommerer
To cite this version:
Noémie Fayeulle, Emmanuelle Meudec, Arnaud Verbaere, Jean Claude Boulet, Clotilde Hue, et al.. Linking cocoa polyphenol composition to chocolate quality with Average-Mass-Spectra fingerprints. CoCoTea 2019 (5. International Congress on Cocoa Coffee and Tea), Jun 2019, Brême, Germany. 2019. �hal-02170450�
Linking cocoa polyphenol composition to chocolate quality with Average-Mass-Spectra fingerprints
Noémie Fayeulle, Emmanuelle Meudec, Arnaud Verbaere, Jean-Claude Boulet, Clotilde
Hue, Renaud Boulanger, Véronique Cheynier, Nicolas Sommerer INRA, Univ. Montpellier, SPO, Montpellier, France
Valrhona SA, Tain l’Hermitage, France
CIRAD, Montpellier, France
Approaches enabling prediction of chocolate quality from cocoa composition would avoid time- and money-consuming steps to chocolate makers. Average mass spectra of
cocoa-polyphenol-extracts led to fingerprints used to select the molecules that discriminate chocolate sensory groups.
16 worldwide cocoa samples were processed into chocolates which were characterized by sensory analysis, allowing sorting of the samples into four sensory groups.
The cocoa polyphenol extracts were analyzed by liquid chromatography−low-resolution mass spectrometry. Averaging each mass spectrum provided polyphenolic fingerprints, which were combined into a matrix and processed with chemometrics (PCA, PLS-DA) to select the most meaningful molecules for discrimination of the chocolate sensory groups.
A larger set of 44 cocoa samples was used to validate the previous results. 29 mass signals of known and unknown molecules, mainly flavan-3-ols, were finally targeted[1], including 2
newly described ethyl-bridged flavan-3-ols[2], enabling sensory-group discrimination. Average
mass spectra fingerprints of cocoa-polyphenol-extracts proved to be quick and efficient to select the molecules that discriminate chocolate sensory groups.
A targeted MRM (Multiple Reaction Monitoring) mass spectrometry method was then developed and validated to routinely analyse large series of cocoa samples.
[1] Fayeulle et al., Fast Discrimination of Chocolate Quality Based On Average Mass Spectra Fingerprints of Cocoa Polyphenols, J Agric. Food Chem., 2019, 67, 2723-2731
[2] Fayeulle et al., Characterization of new flavan-3-ol derivatives in fermented cocoa beans, Food Chem., 2018 (259), 207-212
